
The process by which psychological knowledge advances involves a cycle of theory development, experimental design and hypothesis testing. But after the hypothesis test either does or doesn't reject a null hypothesis, where does the idea for the next experiment come from? Exploratory data analysis completes this research cycle by helping to form and change new theories. After the planned hypothesis testing for an experiment is finished, exploratory data analysis can look for patterns in these data that may have been missed by the original hypothesis tests. Successful exploratory analyses help the researcher modify theories and modify or design novel experiments with focussed hypothesis tests. A second use of exploratory data analysis is in diagnostics for hypothesis tests. There are many reasons why a hypothesis test might fail. There are even times when a hypothesis test will reject the null for an unexpected reason. By becoming familiar with data through exploratory methods, the informed researcher can understand what went wrong (or what went right for the wrong reason). The initial part of the course will introduce the rationale and scope of exploratory data analysis. Next, we will examine how perceptual and cognitive illusions can affect our judgement with respect to exploratory and graphical techniques. Next, we will dive in and try a variety of techniques for the presentation and graphical exploration of univariate, bivariate and multivariate data. We will then use these graphical techniques in the service of other exploratory methods such as data screening, outlier analysis, residual analysis, transformations, and time series analysis. The remainder of the course will be devoted to an integration of these techniques into projects of interest to the students. Computer work associated with the course will primarily involve the Splus software. Additional assignments may introduce the use of Mathematica for visualization of multivariate data. It is expected that students will learn to be sufficiently familiar with Splus that they can access available routines to perform interactive exploratory analyses. Students will also acquire sufficient skill in writing Splus scripts such that they can perform the data manipulations necessary to use exploratory analysis in practical applications to their own research problems.

R Example Files.

Handouts.

Data.

Steven M. Boker Department of Psychology University of Virginia Gilmer Hall Room 102 Charlottesville, VA 22903 Office: 4342437275, FAX: 4349824766 email: boker@virginia.edu 